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Local Pattern Descriptor-based EEG Classification During Mental Arithmetic Tasks: A Comparative Analysis of Machine Learning Models
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 55 |
| Year of Publication: 2025 |
| Authors: Si Thu Aung, Khin Muyar Kyaw, Kyaw Kyaw Oo, Kyaw Kyaw Htut, Min Khant, Kumar, Ei Phyu Moe, Htet Wai Aung |
10.5120/ijca2025925971
|
Si Thu Aung, Khin Muyar Kyaw, Kyaw Kyaw Oo, Kyaw Kyaw Htut, Min Khant, Kumar, Ei Phyu Moe, Htet Wai Aung . Local Pattern Descriptor-based EEG Classification During Mental Arithmetic Tasks: A Comparative Analysis of Machine Learning Models. International Journal of Computer Applications. 187, 55 ( Nov 2025), 41-45. DOI=10.5120/ijca2025925971
Abstract
Cognitive neuroscience explores how brain functions relate to mental processes to better understand cognitive structures. To identify brain states linked to different mental activities, appropriate measurement tools are essential. In this study, a new framework is proposed for classifying mental workload and distinguishing between the resting state and mental counting using local pattern transformations and machine learning algorithms. Mental activities are analyzed using an Electroencephalogram (EEG) via three local pattern transformations: one-dimensional local binary patterns (1D-LBP), one-dimensional local gradient patterns (1D-LGP), and local neighbor descriptive patterns (LNDP). To classify cognitive workload (good vs. bad counters) and resting state versus mental counting, three classifiers are employed: gradient boosting (XGBoost), K-Nearest Neighbors (KNN), and random forests (RF). Using XGBoost and three feature extraction methods, an average performance of about 98% was achieved. With KNN, the highest accuracy was obtained, averaging 99% across all performance metrics with all three feature extraction methods. When using RF, the average score was around 99% with 1D-LBP and 1D-LGP, and 98% with LNDP.
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